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Official PyTorch implementation of VisionHOPE: Visual Backbones as Self-Modifying Learning Systems.
computer-vision backbone image-classification vision-framework backbone-networks foundation-models nested-learning visual-foundation-models
Open source implementation of google's nested learning paper
Google latest research paper "Nested Learning: The Illusion of Deep Learning Architectures" : Implemented in pytorch with handwritten notes. Do give a star if you find this valuable.
NestDetect is a research implementation of class-incremental object detection with YOLO11n.
HOPE: Nested Learning in PyTorch (arXiv:2512.24695) — attention-free language model with self-modifying Titans and a Continuum Memory System
Independent implementation of memory-augmented prompt optimization
efficiency sentence-transformers llm prompt-engineering prompt-optimization deepseek memory-augmented-ai memory-augmented textgrad nested-learning
NestLearnLab — interactive lab on Google's Nested Learning (Behrouz et al., NeurIPS 2025): momentum as associative memory over gradients, deep optimizers, Continuum Memory System & catastrophic forgetting. Real in-browser math.
machine-learning interactive-visualization associative-memory continual-learning optimizers claude-code neurips-2025 ai-built nested-learning daily-webapp
This experiment was inspired by my (so far partial) lecture on Nested Learning: The Illusion of Deep Learning Architecture. The basic idea (at least what I understood) to motivate this experiment is that the Momentum optimizer mechanic is a long-term memory mechanism.
Exploring new trendencies in numerical optimization applied on PINNs. An example with the nonlinear TDSE.
physics quantum-mechanics m3 lbfgs-algorithm tdse physics-informed-neural-networks nested-learning muon-optimizer time-dependent-schrodinger multiscale-momentum-muon
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